ScenGAN: Attention-Intensive Generative Model for Uncertainty-Aware Renewable Scenario Forecasting

• URL: http://arxiv.org/abs/2509.17119v1
• Date: Sun, 21 Sep 2025 15:18:51 GMT
• Title: ScenGAN: Attention-Intensive Generative Model for Uncertainty-Aware Renewable Scenario Forecasting
• Authors: Yifei Wu, Bo Wang, Jingshi Cui, Pei-chun Lin, Junzo Watada,
• Abstract summary: This paper explores uncertainties in the realms of renewable power and deep learning.<n>An uncertainty-aware model is meticulously designed for renewable scenario forecasting.<n>The integration of meteorological information, forecasts, and historical trajectories in the processing layer improves the synergistic forecasting capability.
• Score: 11.600987173982107
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: To address the intermittency of renewable energy source (RES) generation, scenario forecasting offers a series of stochastic realizations for predictive objects with superior flexibility and direct views. Based on a long time-series perspective, this paper explores uncertainties in the realms of renewable power and deep learning. Then, an uncertainty-aware model is meticulously designed for renewable scenario forecasting, which leverages an attention mechanism and generative adversarial networks (GANs) to precisely capture complex spatial-temporal dynamics. To improve the interpretability of uncertain behavior in RES generation, Bayesian deep learning and adaptive instance normalization (AdaIN) are incorporated to simulate typical patterns and variations. Additionally, the integration of meteorological information, forecasts, and historical trajectories in the processing layer improves the synergistic forecasting capability for multiscale periodic regularities. Numerical experiments and case analyses demonstrate that the proposed approach provides an appropriate interpretation for renewable uncertainty representation, including both aleatoric and epistemic uncertainties, and shows superior performance over state-of-the-art methods.

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